Introduction-to-Computer-Vision (1).pptx

Introduction-to-Computer-Vision (1).pptx

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  Computer Vision
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  Introduction to Computer Vision Computervision is a field of artificial intelligence that enables computers to "see" and understand images and videos. It involves mimicking human vision, allowing computers to analyze visual data.
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  What is ComputerVision? Image Acquisition Capturing images using cameras or other sensors. Image Processing Manipulating images to enhance quality or extract features. Image Analysis Interpreting image content and extracting information. Decision Making Making decisions based on image analysis, such as identifying objects or predicting events.
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  Applications of Computer Vision 1Self-Driving Cars Autonomous vehicles rely on computer vision to perceive the environment. 2 Medical Imaging Analyzing medical scans to detect anomalies and assist in diagnosis. 3 Security and Surveillance Identifying individuals, tracking movement, and monitoring security threats. 4 Retail Analytics Analyzing customer behavior, optimizing store layout, and managing inventory.
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  Image Acquisition and Preprocessing 1Image Acquisition Capturing images using cameras or other sensors. 2 Noise Reduction Removing unwanted noise or artifacts from the image. 3 Image Enhancement Adjusting brightness, contrast, and sharpness to improve clarity. 4 Geometric Correction Correcting distortions or perspective shifts in the image.
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  Feature Extraction andDetection Edge Detection Identifying boundaries or sharp transitions between different regions. Corner Detection Finding points where edges intersect or change direction. Blob Detection Identifying regions of uniform color or texture, such as objects or patterns.
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  Image Classification and Recognition FeatureExtraction Extracting relevant features from the image. Feature Matching Comparing extracted features to known patterns. Classification Assigning a label or category to the image.
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  Object Detection and Tracking ObjectDetection Identifying and locating objects in an image or video. Object Tracking Following the movement of detected objects over time.
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  Challenges in Computer Vision LightingVariations Dealing with changes in illumination conditions. Camera Perspective Adapting to different camera angles and viewpoints. Occlusion Handling objects that are partially or completely hidden. Motion Blur Recognizing objects that are moving or blurred.
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  The Future ofComputer Vision Computer vision is rapidly evolving with advancements in deep learning, hardware, and data availability. Expect to see applications in autonomous systems, healthcare, and other domains.
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  Case Study: Computer Visionin Autonomous Vehicles Overview:Computer Vision (CV) enables autonomous vehicles (AVs) to perceive and interpret their surroundings. It processes visual data from cameras to detect objects like pedestrians, vehicles, and road signs, ensuring safe navigation
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  Problem: AVs must accuratelydetect and respond to obstacles in real-time under various conditions (e.g., weather, lighting). Errors in perception can lead to accidents, making reliable object detection critical. Solution: CV techniques like image classification, object detection, and semantic segmentation allow AVs to identify objects and predict their movement. Tesla’s Autopilot system, for example, uses neural networks and cameras to interpret visual data and navigate safely.
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  Challenges: Data Processing: Highcomputational demand for real-time image processing.Adverse Conditions: CV struggles in poor visibility (fog, rain) or low light.Edge Cases: Rare, unexpected scenarios (e.g., animals on the road). Impact: CV enhances AV safety and efficiency by reducing human error. The future lies in improving algorithms, hardware, and sensor fusion for better accuracy, especially in challenging conditions.
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  Computer vision isan evolving field that allows machines to interpret visual data, with applications in areas like autonomous vehicles, facial recognition, and medical imaging. Its advancements promise to enhance everyday life and improve efficiency across various industries. Conclusion
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  Thank You